Learning applications of multi-tasking signal analysis of Fourier transform based on smart mobile devices

This paper presents a viable approach and a new teaching and learning paradigm to enhance the effectiveness of teaching fast Fourier transform [1] and significantly improve the learning outcomes. By using the smart mobile devices, this approach establishes the links among concepts, abstract representations, and realistic applications which are often lacking in traditional instruction. Several mobile apps for real time collection and interactive analysis of the real world objects such as speech signals and images with fast Fourier transform have been developed. These apps penetrate the abstract fast Fourier transform concepts from multiple facets by showing the results obtained in each stage in the fast Fourier transform, the results with different parameter settings, and the comparison of the processing speed between the fast Fourier transform and conventional implementation. These apps enable the students to obtain conceptual understanding through interactive and realistic experiment, guided by mathematical models of the phenomena.